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Checklist for Choosing a Platform

The application of Artificial Intelligence (AI), and machine learning (ML) in particular, has become essential for companies that want to innovate products or services, improve productivity, and disrupt their industry. In order to bring these AI solutions to life, a large amount of high quality labeled data is required to feed and train ML models.

Your ML system is only as good as the data that trains it, so it is especially important to understand the platform technology and tools, the people and processes involved, quality control strategies, and the security and scalability requirements needed for high-quality training data. In choosing a data platform, you need to consider three main aspects:

  1. Your goals
  2. The technology you need to achieve those goals
  3. What the annotators need to know about your project

Download our short guide to choosing a data labeling platform to discover critical aspects to consider when choosing an annotation partner.

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Choosing a data labeling platform and provider requires diligence, but taking the time to find the right partner will be a real advantage in bringing your ML models and AI systems to life. 

This checklist will help you ensure that you choose a provider and platform that is flexible, agile, innovative, and provides top quality data annotations.

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Set yourself up for a long and prosperous partnership by selecting a flexible platform with tools to complete a variety of use cases and a provider with domain expertise, well trained annotators, high security standards, and accountability for the quality of data delivered.

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